FETRA: A Federated Transformer with Dynamic Attention Model for Energy Forecasting in IoBT

Intelligent Energy Prediction in Smart Manufacturing Using Deep Learning Techniques

The transition toward smart manufacturing requires advanced energy management strategies that leverage artificial intelligence to improve operational efficiency and sustainability. This study proposes a novel deep learning framework based on a Long Short-Term Memory (LSTM) network for analyzing and predicting energy consumption in smart manufacturing environments using real-time data acquired from Internet of Things (IoT)-enabled industrial sensors. Unlike previous studies that primarily focus on offline energy forecasting or static datasets, the proposed approach integrates temporal energy consumption patterns from heterogeneous sensor streams to support predictive energy management and dynamic load optimization. The collected data were preprocessed through normalization and feature engineering before being trained and evaluated using the LSTM model. Experimental results demonstrate that the proposed model achieves a Mean Absolute Error (MAE) of 0.84 kWh, a Root Mean Square Error (RMSE) of 2.13 kWh, and a coefficient of determination (R²) of 0.987, indicating high prediction accuracy. Furthermore, the predictive framework enables an estimated energy consumption reduction of 14.8% through proactive load scheduling. These findings demonstrate that integrating LSTM-based deep learning with IoT sensor networks provides an effective solution for intelligent energy forecasting, improves manufacturing efficiency, and contributes to sustainable industrial development.

A Hybrid VMD-AEBiLSTM Model for Load Forecasting in Intelligent Control for Consumer Energy Applications

Explainable AI (XAI) for Intelligent Energy Management in IoT-Enabled Smart Grids

Non-technical losses (NTLs) are a major problem in modern smart grids, damaging revenue and operational functionality. This study proposes an integrated IoT-edge-cloud framework to improve fraud detection, analyze electricity usage patterns, and enhance data reliability in distributed smart grids. The approach extracts multiple features from smart meter data and uses a hybrid machine learning method that combines classification and clustering. To test the system’s robustness, the study included simulated fraud scenarios in difficult circumstances. Results show that the system achieved high detection accuracy (96.4%) and an AUC of 0.98. The framework also reduced false alarms by 86% compared to traditional rule-based methods, improving consistency and productivity. It supports near-real-time operation with response times around 125 ms and is scalable for larger smart grid environments. Behavioral segmentation further improved reliability by identifying differences in electricity consumption and reducing incorrect classifications. Overall, the study shows that combining data quality management, behavioral analysis, and distributed processing yields a more reliable and resilient solution for operational smart grid systems.

Review of emerging smart techniques and their role in enhancing energy efficiency in residential and commercial buildings

This review explores emerging smart technics in enhancing energy efficiency in commercial and residential buildings using systematic and bibliometric approaches from 1990 to 2024. According to the findings, the increase in internet of things applications, like AI and especially machine-learning applications, has enabled smarter buildings in recent years. The advancements of modern data analytics, predictive modelling, and real-time monitoring create a fair base for advancing into new paradigms for energy management. Energy yield prediction and building performance enhancements are ensured through machine-learning techniques, such as ensemble learning, neural networks, and support vector regression. The study found that deep reinforcement learning and fuzzy logic constitute those technologies that automate the consumption behaviors while perfectly balancing efficiency and comfort of occupants. According to the results, smart technologies offer better options toward energy efficiency but encounter major hurdles like poor internet availability, social acceptance, regulatory issues, high upfront cost, scaling issues, and data privacy. Real-time data coupled with smart technology systems should be combined to develop hybrid machine-learning models and predictive energy consumption models. For the attainment of energy efficiency goals, standardization of energy-efficient buildings and greening people's energy practices are key.

Efficient Multivariate LSTM Forecasting of HVAC Energy Consumption in IoT-Enabled Smart Buildings

HVAC systems use up about half of the total energy in smart buildings and are a key focus of optimization. The demand of HVAC energy is very difficult to forecast with high accuracy due to the nonlinear nature of HVAC operations, high temporal variability, and interdependencies among environmental and operational variables. Traditional forecasting methods like regression based models and ARIMA often do not reflect such multivariate dependencies resulting in incompetent energy management. This paper presents a multivariate Long Short-Term Memory (LSTM) model that will be developed to learn the long-term temporal dynamics of various variables related to HVAC. The model is trained and tested on a real-world benchmark dataset, which includes 11 sensor-derived features, and uses one fully connected LSTM layer with 50 hidden units trained using the Adam algorithm. Root Mean Square Error (RMSE) and the coefficient of determination (R2) are reported per variable as measures of forecast performance. The experimental findings indicate that the model is accurate, over 90% on most variables, a fact that justifies the fact that the model is effective in overcoming the weaknesses of the traditional methods and giving accurate predictions that can be incorporated into smart building energy management systems. Further research will focus on hybrid deep learning networks and TinyML networks to run on edge devices that are IoT-enabled.

IOT-BASED ENERGY MANAGEMENT SYSTEM IN ELECTRIC VEHICLES USING OPTIMIZED DEEP LEARNING

Electric vehicles are becoming the backbone of smart mobility in smart city applications because of their potential to reduce carbon footprints. In this research, an IoT-based energy management system for EVs by combining the harbor seal whisker optimization (HSWO) and the improved Elman spike neural network (IESNN) has been proposed. The proposed method uses voltage and current sensors on the battery and supercapacitor to transmit real-time energy parameters to the Blynk IoT platform for remote control and monitoring. The proposed IESNN method is used to predict system power demand. Moreover, HSWO is used to tune the network's weight parameters to improve prediction accuracy. IoT integration enables predictive maintenance and real-time data monitoring, enabling users to remotely assess motor performance, energy usage, and battery health. Compared with existing techniques, HSWO-IESNN improves SoC by 11.9%, 9.3%, 7.3%, 5.6%, and 4.4% over IWHO-DL, SCSO-RERNN, EMCABN-ROA, MRA-SDRN, and FBPINN-SAO, respectively.